291 citations · 936 across the 29 of their papers we have counts for
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Weak pattern convergence for SLOPE and its robust versions
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan
The Sorted L-One Estimator (SLOPE) is a popular regularization method in regression, which induces clustering of the estimated coefficients. That is, the estimator can have coeffic…
Pattern recovery by SLOPE
Małgorzata Bogdan, Xavier Dupuis, Piotr Graczyk +4
SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed some regression coefficient estimates of SLOPE can be null (sparsity) or can be eq…
A Power Analysis for Model-X Knockoffs with -Regularized Statistics
Asaf Weinstein, Weijie J. Su, Małgorzata Bogdan +2
Variable selection properties of procedures utilizing penalized-likelihood estimates is a central topic in the study of high dimensional linear regression problems. Existing litera…
On the asymptotic properties of SLOPE
Michał Kos, Małgorzata Bogdan
Sorted L-One Penalized Estimator (SLOPE) is a relatively new convex optimization procedure for selecting predictors in large data bases. Contrary to LASSO, SLOPE has been proved to…
Group SLOPE - adaptive selection of groups of predictors
Damian Brzyski, Weijie Su, Małgorzata Bogdan
Sorted L-One Penalized Estimation is a relatively new convex optimization procedure which allows for adaptive selection of regressors under sparse high dimensional designs. Here we…
False Discoveries Occur Early on the Lasso Path
Weijie Su, Malgorzata Bogdan, Emmanuel Candes
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the importa…